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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Innovation for advanced fuels at SRNL
As the only Department of Energy Office of Environmental Management–sponsored national lab, Savannah River National Laboratory has a history deeply rooted in environmental stewardship efforts such as nuclear material processing and disposition technologies. SRNL’s demonstrated expertise is now being leveraged to solve nuclear fuel supply -chain obstacles by providing a source of high-assay low-enriched uranium fuel for advanced reactors.
Josh Peterson, Bret van den Akker, Riley Cumberland, Paul Miller, Kaushik Banerjee
Nuclear Technology | Volume 199 | Number 3 | September 2017 | Pages 310-319
Technical Paper | doi.org/10.1080/00295450.2017.1318595
Articles are hosted by Taylor and Francis Online.
The U.S. Department of Energy, Office of Nuclear Energy is sponsoring development of a database to store information related to spent nuclear fuel (SNF) in support of its Spent Fuel and Waste Disposition efforts. This database, referred to as the Unified Database (UDB), is part of a larger engineering analysis tool, the Used Nuclear Fuel Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). The UDB provides a comprehensive, controlled source of SNF information, including dry cask attributes, assembly data, economic attributes, transportation infrastructure attributes, potential future facility attributes, and federal government radioactive waste attributes. There are a number of existing and envisioned data reports that can be expected to use data stored within the UDB; however, previously, there was not a streamlined method to couple the database to such data reports. Therefore, to streamline the creation of these reports, two methods were developed to generate documents from information in the database automatically. The first method used Java and LaTeX for automatically generating the report, and the second method used the Python programming language along with Sphinx, a Python documentation generator. There are some advantages and disadvantages to both approaches, but both methods produced equally high-quality, automatically generated reports that were directly coupled to the database. This paper describes data currently available in the UDB; explains the two different methods for automatically generating reports from these data; and shows examples of inline text, figures, and tables automatically generated using both approaches.